Network Travel Research Articles

Methods for Rapid Mobility Estimation to Support Outbreak Response.

When pressed for time, outbreak investigators often use homogeneous mixing models to model infectious diseases in data-poor regions. But recent outbreaks such as the 2014 Ebola outbreak in West Africa have shown the limitations of this approach in an era of increasing urbanization and connectivity. Both outbreak detection and predictive modeling depend on realistic estimates of human and disease mobility, but these data are difficult to acquire in a timely manner. This is especially true when dealing with an emerging outbreak in an under-resourced nation. Weighted travel networks with realistic estimates for population flows are often proprietary, expensive, or nonexistent. Here we propose a method for rapidly generating a mobility model from open-source data. As an example, we use road and river network data, along with population estimates, to construct a realistic model of human movement between health zones in the Democratic Republic of the Congo (DRC). Using these mobility data, we then fit an epidemic model to real-world surveillance data from the recent Ebola outbreak in the Nord Kivu region of the DRC to illustrate a potential use of the generated mobility estimation. In addition to providing a way for rapid risk estimation, this approach brings together novel techniques to merge diverse GIS datasets that can then be used to address issues that pertain to public health and global health security.

Estimating network travel time reliability with network partitioning

Network travel time reliability can be represented by a relationship between network space-mean travel time and the standard deviation of network travel time. The primary objective of this paper is to improve estimation of the network travel time reliability with network partitioning. We partition a heterogeneous large-scale network into homogeneous regions (clusters) with well-defined Network Fundamental Diagrams (NFD) using directional and non-directional partitioning approaches. To estimate the network travel time reliability, a linear relationship is estimated that relates the mean travel time with the standard deviation of travel time per unit of distance at the network level. The impact of different partitioning approaches, as well as the number of clusters, on the network travel time reliability relationship are also explored. To estimate individual vehicle travel times, we use two distinct approaches to allocate vehicle trajectories to different time intervals, namely trajectory and sub-trajectory methods. We apply the proposed framework to a large-scale network of Chicago using a 24-h dynamic traffic simulation. Partitioning and travel time reliability estimation are conducted for both morning and afternoon peak periods to demonstrate the impacts of travel demand pattern variations. The numerical results show that the sub-trajectory method for the network travel time reliability estimation and the directional partitioning with three clusters have the highest performance among other tested methods. The analyses also demonstrate that partitioning a heterogeneous network into homogeneous clusters may improve network travel reliability estimation by estimating an independent relationship for each cluster. Also, comparing morning and afternoon peak periods suggests that the estimated parameter for the linear network travel time reliability relationship is directly related to the coefficient of variation of density as a measure of spatial distribution of congestion across the network.

Inns And Elite Mobility In Late Georgian Britain*

Abstract This paper considers the significance of the spaces and material culture of the ‘principal inn’ as the centre of a distinct world of elite mobility in eighteenth-century Britain. Inns were central to the expansion and improvement of the travel network that brought the British Isles closer together through the long eighteenth century. The turnpike system introduced improved surfaces to old and new roads while new coach-building technology allowed faster movement on those roads. However, it was the national network of inns, regularly and reliably punctuating Britain's roads, that made fast and efficient travel a practical, everyday reality from London to York, Bristol to Holyhead, Edinburgh to Inverness. On arrival the inn provided food and accommodation for travellers, hay and stables for horses and grease for carriage axles. From cross-country travel to crossing the inn-yard, finding a table in the parlour or climbing the stairs to bed, the inn served the traveller across different scales of space and mobility. Moreover, for the elite traveller, inns were not simply blank containers for travel-related activities; they were material constructs that gave those activities form and meaning. Within the principal inn refined interior spaces and well-made, fashionable things placed the elite traveller in a reassuringly familiar cultural space, a bubble of comfort, luxury and good taste which they did not leave from one inn to the next.

The consequences of migration for the migrants' parents in Bolivia

AbstractThe existing literature on transnational care suggests that it is possible to care for ageing parents from afar. However, most of these studies are based on research in higher‐income countries, where families have access to institutional support and where travel and communication networks are generally of high quality. Studies focusing on lower‐income countries of origin have found a greater likelihood of migrants' parents being in a vulnerable situation. Here, though, there has been a preference for focusing on rural areas. In this article, we address this gap by reporting on a research project that spans rural, urban and peri‐urban areas, as well as different migration streams (regional South–South and South–North), and ask how migrants' parents fare during their adult children's absences in a context of very weak institutional support, widespread poverty and inequality. We find that parents in urban and peri‐urban areas are generally better able to negotiate their adult children's absences, while those in rural areas experience an increase in their vulnerability. However, there are also stories that are in tension with these general trends, which indicates that social networks, migrants' destinations and parents' socio‐economic statuses also mediate the availability of transnational care.

Does India need ministry of public health? – Lessons learnt from COVID-19 and road ahead

World over there has been debates on the current emer­gency of COVID-19 and where we failed? Comparing the Spanish Flu 1918 (1) and the COVID-19, it is clear that the prevention strategies like social distancing, hand washing, and mask use, remains only hope for fast-spreading emergencies or disasters like COVID-19 (2). As compared to Spanish Flu-1918, population density is more now and people move across the globe much faster due to the vast network of air travel, posing greater risk to exposed populations. Even today, the challenge remains the lack of vaccines, shortage of appropriate equipment, and healthcare manpower. The basic role of the World Health Organisation and Alma Ata Declaration 1978 was strengthening primary and preventive care by the year 2000 (3). However, the world supported technology to the extent that now we are left with the only option to lockdown city after city across more than 200 counties.

Suppressing the effects of induced traffic in urban road systems: impact assessment with macrosimulation tools - results from the city of Krakow (Poland)

This paper presents results from simulation analysis of road network development scenarios in the city of Krakow (Poland), accounting for potential impact of traffic demand elasticity. These envisage a completion of a suburban ring road, which would improve network travel conditions and could therefore induce additional traffic volumes (and eventually lead to further traffic congestion). The objective was to investigate how this effect might be inhibited by introducing complementary changes in the city road network, ranging from individual road narrowing schemes in the city centre to “bolder” measures comprising network-wide traffic calming and road closure scenarios. Our macrosimulation-based analysis reveals that, once accounting for demand elasticity phenomena, city-wide benefits are roughly similar for all the considered scenarios, but a durable traffic congestion relief could be only achieved with a consequent reduction of inner-city network capacity. Our findings indicate how the appraisal process of urban road projects could be facilitated with an estimate of potential demand elasticity effects, as effectiveness of such schemes needs to be assessed not just in terms of level-of-service improvements, but also their exposure (or resilience) to erosion in scheme benefits due to probable induced traffic phenomena.

СОГЛАСОВАНИЕ РАСПИСАНИЙ АВТОБУСНЫХ МАРШРУТОВ В ОБЛАСТНЫХ ПАССАЖИРСКИХ ПЕРЕВОЗКАХ

In modern conditions of the Russian Federation in the field of traffic management, one of the priorities is to develop and improve the quality of functioning of route transport. One of the main requirements of passengers is the minimum time spent on the trip. It is problematic to meet this requirement without mutual coordination of transport processes along routes and the use of stopping points by vehicles and passengers due to their interdependence. The problem of such coordination is not only in cities, but also in regional settlements. The subject of this study is the coordination of passenger transport processes on regional bus routes that have common sections and stop-and-transfer points. The purpose of the study is to improve the organization of passenger transportation in regional directions by coordinating routes in time. In this study, we developed a method for coordinating bus schedules for regional routes that share common sections of the route or stop-and-transfer points. According to the results of applying the method for the Sargatsky district of the Omsk region, the results were obtained, according to which the average waiting time for a passenger in the case of a transfer was reduced by 60 %. Application of the developed method allows to organize rational distribution of arrival and departure of buses during the day according to the criterion of the minimum waiting time for passengers at the stop and transfer point and allows to solve the problem of inconsistency of bus traffic. Experimental testing has shown that the use of this technique reduces the waiting time for passengers at transfer points by up to 60%, which also reduces the time of network travel. The developed method can be used in the organization of intercity, intermunicipal and rural passenger transport.

Optimal Multimodal Travelway Design for an Urban Street Network

This study introduces a bi-level model for optimal travelway design of an urban street network by successively executing a lower-level model for traffic assignments and an upper-level model for network travel time minimization. A computational experiment is conducted for optimal travelway design of a 4-square-km urban street network containing 25 signalized intersections, 80 street segments, and 5 bus routes that accommodates 62,640, 43,200, and 33,120 person-trips per hour in AM/PM peak, adjacent-to-peak, and off-peak periods, respectively. Model execution results indicate that adopting a higher number of narrow lanes for auto use only and auto/bus shared use could potentially lead to increases in auto mode share and savings of network total travel time. More narrow lanes for auto use could raise auto speeds, but the auto/bus shared use of narrow travel lanes could slightly fluctuate bus speeds. Further converting narrow lanes for shared use by autos and buses to exclusive bus lanes (EBLs) could enlarge bus mode share, reduce network total travel time, slightly elevate auto speeds, and drastically increase bus speeds. The proposed model could be augmented to incorporate optimization of networkwide intersection signal timing plans, bus signal priorities, and bus dispatching frequencies into optimal travelway design.

Research on travel network structure based on normalised Laplacian spectrum

To study the residents' trip-activity chain and patterns, we start from the residents' trip-activity network. The normalised Laplacian spectrum from the local metric features described the topological structure information of network with the statistical analysis method of complex network. The statistical analysis of complex networks failed to show the global information of the network and described the network structure more completely, intuitively and conveniently. The normalised Laplacian spectrum is used to portray the subway network, the airline network and the macro-laws of the network from residents travel. In this paper, we analysis other networks and classify networks qualitatively. The results proved that the normalised Laplacian spectra are an efficient tool for analysing macro-structural or micro-structural features of geographic networks.

Simultaneous line planning and timetabling based on a combinational travel network for both trains and passengers: a mixed-integer linear programming approach

This paper is devoted to designing a mixed-integer linear programming to solve the simultaneous line planning and timetabling problem. A directed graph is first constructed to represent the combinational travel network for both candidate trains and passengers. Through this graph, it is easy to simultaneously describe trains’ operation choices, passengers’ travel choices, and their restrictive relations. Based on it, a mixed-integer linear programming model with many binary and non-negative integer decision variables is built to simultaneously optimize the line plan, timetable, and passenger travel choices, which can be efficiently solved using commercial solver CPLEX for small-scale network, while a decomposition strategy is required to combine with CPLEX for large-scale network. A set of numerical experiments are carried out to analyse the impacts of flow sub-units, number of candidate trains, and weight factors. Finally, we conduct two case studies for testing the practicability of our proposed method.

Research on travel network structure based on normalised Laplacian spectrum

To study the residents' trip-activity chain and patterns, we start from the residents' trip-activity network. The normalised Laplacian spectrum from the local metric features described the topological structure information of network with the statistical analysis method of complex network. The statistical analysis of complex networks failed to show the global information of the network and described the network structure more completely, intuitively and conveniently. The normalised Laplacian spectrum is used to portray the subway network, the airline network and the macro-laws of the network from residents travel. In this paper, we analysis other networks and classify networks qualitatively. The results proved that the normalised Laplacian spectra are an efficient tool for analysing macro-structural or micro-structural features of geographic networks.

Modeling and Prediction of Ride-Sharing Utilization Dynamics

The potential of an efficient ride-sharing scheme to significantly reduce traffic congestion, lower emission level, and drivers’ stress, as well as facilitating the introduction ofsmart citieshas been widely demonstrated in recent years. Furthermore, ride sharing can be implemented within a sound economic regime through the involvement of commercial services that creates a win-win for all parties (e.g.,Uber,LyftorSidecar). This positive thrust however is faced with several delaying factors, one of which is the volatility and unpredictability of the potential benefit (or utilization) of ride-sharing at different times, and in different places. Better understanding of ride-sharing dynamics can help policy makers and urban planners in increase the city’s “ride-sharing friendliness” either by designing new ride-sharing oriented systems, as well as by providing ride-sharing service operators better tools to optimize their services. In this work the following research questions are posed: (a) Is ride-sharing utilization stable over time or does it undergo significant changes? (b) If ride-sharing utilization is dynamic can it be correlated with some traceable features of the traffic? and (c) If ride-sharing utilization is dynamic can it be predicted ahead of time? We analyze a dataset of over 14 million taxi trips taken in New York City. We propose a dynamic travel network approach for modeling and forecasting the potential ride-sharing utilization over time, showing it to be highly volatile. In order to model the utilization's dynamics, we propose a network-centric approach, projecting the aggregated traffic taken from continuous time periods into a feature space comprised of topological features of the network implied by this traffic. This feature space is then used to model the dynamics of ride-sharing utilization over time. The results of our analysis demonstrate the significant volatility of ride-sharing utilization over time, indicating that any policy, design, or plan that would disregard this aspect and chose a static paradigm would undoubtably be either highly inefficient or provide insufficient resources. We show that using our suggested approach it is possible to model the potential utilization of ride sharing based on the topological properties of the rides network. We also show that using this method the potential utilization can be forecasting a few hours ahead of time. One anecdotal derivation of the latter is that perfectly guessing the destination of a New York taxi rider becomes nearly three times easier than rolling a “Snake Eyes” at a casino.

Hierarchical ramp metering in freeways: An aggregated modeling and control approach

This paper develops a model-based hierarchical control method for coordinated ramp metering on freeway networks with multiple bottlenecks and on- and off-ramps. The controller consists of two levels where at the upper level, a Model Predictive Control (MPC) approach is developed to optimize total network travel time by manipulating total inflow from on-ramps to the freeway network. The lower level controller distributes the optimal total inflows to each on-ramp of the freeway based on local traffic state feedback. The control method is based on a parsimonious aggregated traffic model that relates the freeway total outflow to the number of vehicles on the freeway sections.Studies on aggregated traffic modeling of networks have shown the existence of a well-defined and low-scatter Macroscopic Fundamental Diagram (MFD) for urban networks. The MFD links network aggregated flow and density (accumulation). However, the MFD of freeway networks typically exhibits high scatter and hysteresis loops that challenge the control performance of MFD-based controllers for freeways. This paper addresses these challenges by modelling the effect of density heterogeneity along the freeway and capacity drop on characteristics of freeway MFD using field traffic data. In addition, we introduce a model to predict the evolution of density heterogeneity that is essential to reproduce the dynamics of freeway MFD accurately. The proposed model is integrated as the prediction model of the MPC in the hierarchical control method.The proposed coordinated ramp metering method shows desirable performance to reduce the vehicles total time spent and eliminate congestion. The control approach is compared with other coordinated ramp metering controllers based on the MPC framework with different traffic prediction models (e.g.CTM and METANET). The outcomes of numerical experiments highlight that the MFD-based hierarchical controller (i) is better able to overcome the modeling mismatch between the prediction model and the plant (process model) in the MPC framework and (ii) requires less computation effort than other nonlinear controllers.

Disaster and unplanned disruption: Personal travel planning and workplace relocation in Christchurch, New Zealand

Christchurch, New Zealand, is a city dominated by car use. In the 2013 Census 84% of people travelled to work by car. In 2010 and 2011, a series of earthquakes resulted in widespread damage to horizontal infrastructure, and the relocation of many homes and businesses. Over 70 percent of buildings in the central business district were destroyed or deconstructed and many organisations, including government departments and private commercial enterprises, relocated to suburban areas.The relocation of businesses back to the central city in 2017 presented opportunities to rethink the travel network to and from the city, and to promote alternatives to private motor vehicle use such as public transport, car-pooling, walking and cycling. The Greater Christchurch Partnership undertook personal travel planning with some of these organisations to help facilitate the goals of the new transport plan. This action was based on the habitat discontinuity hypothesis that context change or disruption increases the likelihood that a behaviour will be reconsidered and different choices made.A personal travel planning programme was subsequently implemented. It included a pre-move survey (N = 834), individual interviews with staff (N = 1234) and a post-move survey (N = 624). Results showed that after businesses relocated to the central city, the mean number of trips per week made by walking, cycling, bussing and carpooling increased for those not interviewed (Group A) and those interviewed (Group B) and the mean number of trips by car per week decreased for both these groups.Results of the ANOVA showed the difference in mean trips per week between the group interviewed and the group not interviewed was statistically significantly higher for bussing after businesses moved. No other statistically significant differences were found between these two groups for any other mode.It is concluded that one-to-one interviews targeting workers about to relocate were effective in relation to bussing, but were not effective in relation to cycling, carpooling, walking and 'other' forms of transport. This study provides a unique situation involving large scale business relocation, rather than the fragmented relocation of individual residents.

Debunking the Smuggler-Terrorist Nexus: Human Smuggling and the Islamic State in the Middle East

Amid increasing terrorist violence in and beyond European countries, concerns have been raised about connections between illegal migration and terrorism. Regional armed conflicts in the Middle East have led to the massive migration of people in search of safe heavens and better livelihoods, pressing upon frontline countries in the Mediterranean and throughout the EU. Multiple government and intelligence agencies report that human smuggling networks have been identified as providing a readily available conduit through which terrorist groups such as the Islamic State and Al-Qaida can enter Europe and the U.S. These criminal travel networks are said to rely on highly effective transnational alliances involving service providers within source, transit and destination countries. There is also widespread consensus in the intelligence circles that terrorist groups rely on the practice of smuggling for financing of terrorist activity. Nonetheless, despite the region’s geopolitical significance and its demonstrated potential for spillover effects, scant systematic field research has been conducted by independent researchers to understand the purported nexus between terrorism and human smugglers within the Middle East into the Mediterranean. This constitutes a severe gap in knowledge which our study will address. In this paper, we debunk the nexus human smuggling-terrorism by comparing the Islamic State’s logistics with human smuggling networks’ modus operandi and organizational structures. Based on a mixed research approach that combines the analysis of a unique date-set (U.S. Special Forces) and an empirical research carried out among smugglers and migrants in the Middle East and across the Eastern Mediterranean route over the past two years, this paper will tackle the alleged connection between human smuggling and terrorist groups. What will be argued is that smuggling networks and terrorist networks have fundamental operational and structural differences. These operational and structural differences need to be taken into account in order to deconstruct harmful stereotypes on irregular migration and, consequently, develop adequate responses to analytically distinct phenomena.

Multi-class AR model: comparison with microsimulation model for traffic flow variables at network level of interest and the two-dimensional formulation

ABSTRACT This paper discusses on the choice of a recently proposed multi-class, speed-area occupancy based, one-dimensional continuum model towards the measurement of network level traffic flow variables, namely, travel time and outflow. The parameters of the model are calibrated for a hypothetical mid-block with four vehicle classes, using the micro simulation data from VISSIM software. Analysis result indicates that the model produces travel time and outflow result closer to that by the VISSIM micro simulation. To explore this macroscopic model towards accurate modelling of lacking lane disciplined traffic, i.e. the traffic with frequent lateral movements, a two-dimensional formulation of the model is proposed. It is shown that the new formulation retains the properties of its original version as well as the desirable properties of any traffic flow model.

Bounding the Inefficiency of the Reliability-Based Continuous Network Design Problem Under Cost Recovery

This study defines the price of anarchy for general reliability-based transport network design problems, which is an indicator of inefficiency that reveals how much the design objective value exceeds its theoretical minimum value due to the risk averse and selfish routing behavior of travelers. This study examines a new problem, which is a reliability-based continuous network design problem under cost recovery. In this problem, the variations of system travel time and path travel times, the risk attitudes of the system manager and travelers, congestion toll charges, capacity expansions, and cost recovery constraint are explicitly considered. The design problem is formulated as a min-max problem with the reliability-based user equilibrium constraint. It is proved that the price of anarchy for this problem is bounded above, and the upper bound is independent of travel time functions, demands, and network topology. The upper bound is related to the travel time variations, the value of reliability, and the value of time.

An efficient ride-sharing recommendation for maximizing acceptance on geo-social data

Ride-sharing, which refers to assigning a set of riders for saving travel miles and alleviating traffic pressure, has drawn increasing attention. Existing works emphasize compatibility of potential riders on the basis of geographic proximity. They generally assume that no rejection would happen after the assignment is completed by the server. However, ignorance of psychological factors on ridesharing (e.g., trust on car mates) can lead to decrease rider acceptance. Thus, in this paper, we take the tendency of a rider to group with others into consideration and maximize riders’ acceptance when sharing a trip. Specifically, we formally define the problem of maximizing riders’ acceptance based on people′s interests, social links, and employ social networking to facilitate finding a ridesharing group for the rider with the largest acceptance. We propose a new ride-sharing mode to recommend groups that travel together from geo-social data streams. To optimize the recommendation, we develop a heterogenous travel network, based on a proposed destination-prediction algorithm, to mine the similar spatial movements among a set of riders. Then, we measure the willingness of riders for joining in a group using social context. Finally, we progressively select the riders with high acceptance to be in the top-k results. We present the results of applying framework on real world social media data from the Twitter. Computational results show our method is able to significantly reduce the travel time when ridesharing, while keeping a high level of acceptance on real-world datasets.

Optimization of traffic count locations for estimation of travel demands with covariance between origin-destination flows

Vehicular traffic between different Origin-Destination (OD) pairs for a typical hourly period may statistically correlate with each other. The covariance mainly generated from the daily variation of travel patterns, network topology, and trip chaining activities of household members can be particularly high during the morning peak hour. With the increasing attention on the OD demand variance and covariance in stochastic road networks, a new criterion is proposed in this paper for measuring the estimation accuracy of OD demand covariance. The mathematical properties of this proposed criterion are analyzed to better understand the relationship between the estimation errors of mean and covariance of OD demands. This paper aims to investigate how to optimize the traffic count locations for minimizing the weighted maximum deviation of estimated mean and covariance of OD demands from the “true” values. To consider the effects of stochastic OD demands on the traffic count location problem in the proposed model, link choice proportions are regarded as stochastic variables and updated by an adapted traffic flow simulator in this study. Both the weighted-sum approach and bi-objective approach are examined with the adaption of the firefly algorithm (FA) to solve the single-objective and bi-objective problems. Numerical examples are presented to demonstrate the effects, with and without considering the covariance of the OD demands for the optimization of traffic count locations.

A Novel Self-Organizing Approach to Automatic Traffic Light Management System for Road Traffic Network

Traffic network is basically a “network of networks” consisting of mainly two types of networks: road network and a travel network. Due to drastic increase in population of vehicles, traffic congestion in metro cities of India is a severe problem. To resolve this use, we have proposed an approach to dynamic and automatic road traffic light management system. The approach is based on arithmetic mean theorem. The input to the control interface of traffic light of proposed approach are six different parameters : velocity of traffic units (v), queue length (l), inter-arrival time between vehicles (t), centrality measures value(c) and predicted value (prdt v) of traffic congestion by historical database and output is level of congestion. On the basis of congestion level, we build inference rules. The proposed approach automatically updates duration of green and red light as per the level of congestion at a particular junction. Proposed approach follows forward reasoning of If–Then rules. A study of this approach is done in this paper on various traffic situations of Delhi, India depending on queue length of vehicles.